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The ROI of Big Data for Marketers

Chief Marketing Officers know the benefits of Big Data. Oftentimes what they don’t know is how to use it. David Rogers and Don Sexton at the Columbia Business School wanted to gain a better understanding of the changing practices among large corporate marketers. What they found was support for the use of new data to drive marketing decisions and measuring ROI and a widespread adoption of new digital tools.

Still, significant gaps exist between conception and execution when it comes to Big Data Marketing efforts and there remains a need to improve on the use of data, the measurement of digital marketing and the assessment of ROI.

Successful brands use customer data to drive marketing decisions, 91% of senior corporate marketers

Yet, 39% say their own company’s data is collected too infrequently or not in true real-time

A lack of sharing customer data within their own organization is a barrier to effectively measuring marketing ROI, according to 51% of respondents

Around 85% of large corporations maintain brand accounts on social networks such as Facebook, Twitter, Google+ and Foursquare

Comparison of the effectiveness of marketing across different digital media is “a major challenge” for 65% of marketers

Financial outcomes where omitted by 37% of respondents when asked to define what “marketing ROI” meant for their own organization

57% of respondents are not basing their marketing budgets on any ROI analysis

Brand awareness is the sole measure to evaluate marketing spend for 22% of marketers

Source: Marketing ROI in the Era of Big Data: The 2012 BRITE/NYAMA Marketing Transition Study

Do You Know Your Customers As Well As You Think You Do?

Anthony Perez is the Vice President of Business Strategy with the Orlando Magic. He and his team are credited with building a predictive analytics ticketing model that can help the Orlando Magic tailor their fan experience to increase customer satisfaction. In this interview, Anthony shares how he uses data to gain understanding of his customers and to establish a loyalty connection.

hrh media: Would you mind telling us a little bit about your background and how you became interested in data analytics?

I started in finance so I think it was a natural transition into analytics. When I first started with the Orlando Magic I was really focused on the financials of the new arena. From there I went into investment banking for a little while, but ultimately came back to the Magic and my focus, again, started out on the financial metrics side of things. But my position ultimately became the much broader role around analytics that it is today.

hrh media: The Orlando Magic is both one of the most technologically advanced arenas in North America so it’s no wonder that you have a wealth of data at your fingertips. Would you share a key business driver behind the Magic’s decision to use analytics to establish a loyalty connection with fans?

Sure, for us it just really made sense going into the Amway Center with the technology that we have, all the data that we’d be collecting, all of the capabilities that we had in terms of leveraging the technology and the building, we really wanted to make sure that we were doing the best job that we could to drive the customer experience. So, analytics was an opportunity for us to expand the things that we were doing in a much more systematic way to make sure every fan felt like there was a customized experience for them and that we are really marketing to them and what they were looking for. For our season ticket holders in particular, we wanted to make sure that when they came to games and interacted with their service representative or anyone else from the team, that they felt like the experience really spoke to them and their preferences and the like. So that was the main driver behind taking a bigger step into the realm of analytics.

hrh media: How have you and your team been able to turn the data that you’ve collected from the scanned and tracked tickets into a tailored fan experience? Can you provide me with an example?

That’s something that we’re continuing to develop. A great example, in particular for season ticket holders, but really for anyone in the building, we run a program where if you purchase something at a concession stand or at one of our retail stores you can scan your ticket during the checkout process and get entered in for a chance to win courtside seats. What this allows us to do is capture the ticket barcode information and ultimately match that back to the account. So we can see what account purchased the various items for that transaction. We start to see trends around what people are buying and when they’re buying.

The season ticket holders are folks that are coming to the majority of our games, so we can get a feel for when they typically purchase merchandise throughout the season and what their preferences are in terms of concession stands. A great example we like to point to is if a family of season ticket holders is taking frequent trips to Cold Stone at halftime on the terrace level, if we see that they have a preference for Cold Stone, that’s something our service rep can use the next time they want to visit that season ticket holder in their seats. Maybe they’re surprising them with Cold Stone, bringing it to them just as a gesture to say that we really appreciate them being a season ticket holder and everything they do for us. I think that’s the small example but that’s the type of thing that we continue to do.

Right now we’re working through taking all that information that we’re collecting from scanned tickets, putting it into a format that’s actionable for our sales team and service team and ultimately delivering that.

hrh media: Retaining season ticket holders year after year can be pretty tricky for sports franchises. Have you been able to solve this riddle with somewhat of a success rate using data analytics?

I don’t know that anyone will ever solve the riddle completely, but I think for us, we’ve done a good job of being as smart as we can about how we target season ticket holders and how we focus on retention. We’ve done a lot in that area in terms of predictive modeling and trying to understand the biggest drivers behind the renewal decision for a season ticket holder and ultimately boiling that down to things that are actionable and then working with our service team to act upon those things.

A great example is ticket utilization, which I think for everybody is pretty intuitive, but we’ve really focused on that and what we found is that season ticket holders that aren’t really utilizing their tickets to a certain level are ultimately ones that are at risk not to renew. We’ve built an entire campaign throughout the season about touching those season ticket holders that aren’t utilizing their tickets and doing it in a way that we can be preemptive so we’re not finding out that they felt like the value wasn’t there for them because they didn’t use their ticket throughout the season.

Instead we’re trying to jump on that if they’ve missed one or two games, and if we escalate the actions we take as they go further into the season and not utilizing their tickets. I think for us it varies from team to team in terms of how the structure is set up for a service representative and the amount of accounts they deal with. For us, one of our service representatives could have anywhere between 400 to 500 accounts, so it’s really difficult to build a personal relationship with each one of those people, so what can we do to help our service representatives cut through all of their accounts to really get the biggest impact and focus on those people who need or want the additional action—that’s where we try to use predictive analytics to help us do that.


hrh media: Are these actions that you’re taking being noticed and appreciated by the ticket holders?

I think so. For us, with ticket utilization, it’s really making sure that season ticket holders feel like they’re getting the value for their tickets in whatever way that means. It may mean selling their tickets for games they can’t attend or it could just mean giving them away to a client, colleague or friend, but we’re just trying to make sure that we’re proactively driving those actions and making sure that it’s not something that the season ticket holder looks back when it’s up for renewal; and in retrospect says, well if I’d done things differently maybe I would have gotten the value, but I didn’t.

I do think they appreciate it and I think they’ll see that as we continue to go deeper and deeper into how we’re leveraging our data I think they’ll continue to see a better experience game after game.

hrh media: What does the future look like for customer data analysis within your franchise?

I think the future is really exciting for us. We do a lot of really interesting things now and I think we’re a team that’s really trying to push the envelope and be more advanced in terms of how we’re applying analytics. Even from our standpoint I think there are so many things that we can continue to do, there’s a lot of unstructured data that we’re really not leveraging as well as we could, and social media is a whole frontier of opportunity for us in utilizing data. Even just outside of social media when we talk about unstructured data, a lot of communications with our season ticket holders are happening through email correspondence, text messaging and those types of things. So how do we really leverage those kinds of conversations because right now without any type of text mining application, we’re really not able to maximize the insights we can draw. We do a lot of research here and even with surveys we see the open-ended responses and we try to leverage that as best as we can. I think there’s a lot of opportunity there in terms of text mining and just utilizing unstructured data better, whether it’s in our predicted models or just driving key insights for our management.

The Top 13 HR Metrics For 2013

Human capital is a company’s biggest asset, but it can also be its biggest liability. Businesses can be made or broken by the quality of its employees. Success, therefore, is often a matter of attracting the best new recruits while also ensuring the happiness of current employees in order to retain the strongest personnel.

Human resources professionals are placing a large emphasis on reducing administrative costs while increasing productivity and job satisfaction. Not surprisingly, retention and cost control are at the top of the list of employer objectives, according to the 7th annual MetLife Study of Employee Benefits Trends.

With fewer resources and more pressure to produce, human resources professionals are tasked with identifying what their employees are most concerned about in their work life in an effort to ensure their loyalty. Beyond salary and wages, the most important factor in employee loyalty is health, retirement and insurance benefits. Advancement opportunities and company culture also play a large part in employee loyalty, according to the survey.

So, how can human resources professionals align their employer’s objectives of making and saving money with their employee’s priorities? One way is by the use of human resources metrics.

In order to streamline your company’s goals, human resources should focus on four focus areas; recruiting, retention, staffing and training, and development, according to CoreCentive, a human capital management consulting firm.

Recruiting metrics quantify new hire performance, the impact of a poor new hire as well as turnover rates and return on investment. Retention metrics quantify turnover rates in addition to average tenure and the worth of veteran workers.

Training and development metrics measure training process time and costs and how professional development processes help businesses achieve their business goals. Staffing metrics measure cost per hire, recruiting efficiency ratio and the cost to replace an employee.

Below is a list of the most important human resources metrics your company should be looking into in 2013.

The Top 13 HR Metrics for 2013

Absence Rate – Shows how many days your workers are missing, which could be an indication of their satisfaction.
The number of days absent per month / (average number of employees during a month x the number of workdays)

Benefit Cost – Determine the cost of benefits packages per employee.
(Total cost of employee benefit / total number of employees)

Benefit as a Percent of Salary – Determine the cost of benefits as the percentage of an employee’s salary.
(Annual benefits cost / Annual salary)

Cost Per Hire – How much does your organization really spend per new hire?
(Recruitment costs / (compensation cost + benefit cost)

Performance Goals – The percentage of performance goals met or exceeded
(The number of performance goals met or exceeded / Total number of performance goals)

Return on Investment – What is the organization’s ROI per employee?
(Total benefit – total costs) x 100

Revenue Per Employee – Measure how much each employee earns for the company.
(Revenue / Total number of employees)

Satisfaction – Tracking employee satisfaction is difficult, but surveys can help you gauge this metric.

Tenure – Determine the average amount of time an employee has been with the company.
(Average number of years of service at the organization across all employees)

Time to Fill – What is the cost of the time it takes to fill open positions?
(Total days taken to fill a job / Number hired)

Training Development Hours – Streamline your professional development costs.
(Sum of total training hours / total number of employees)

Turnover – Spells out how many employees depart your organization per year
(The number of employees exiting the job during a one year period / Average actual number of employees during the same period)

Turnover Costs – How much money are you losing when an employee leaves? Vacancy, new hiring and new training costs can add up.
(Total costs of separation + vacancy + replacement + training)
Source: Employer’s Resource Council, CoreCentive